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CCS enabled drug metabolite profiling and characterization workflow for distinguishing drug metabolites
EP38450
Poster Title: CCS enabled drug metabolite profiling and characterization workflow for distinguishing drug metabolites
Submitted on 09 Mar 2022
Author(s): Xuejun Peng 1 , Lucy Woods 2
Affiliations: 1 Bruker Scientific LLC, San Jose, CA; 2 Bruker Scientific LLC, Billerica, MA
Poster Views: 222
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Poster Information
Abstract: Fast and accurate identification and characterization of drug metabolites play a critical role in preclinical and clinical development stages to assist lead compound structure optimization, screening drug candidates, and finding active or potentially toxic metabolites. In this work, a DDA PASEF non targeted LC timsTOF Pro metabolomics workflow was conducted to profile and characterize drug metabolites. Metabolites were postulated by utilizing BioTransformer[1], a knowledge and machine learning based approach to predict small molecules metabolism. Metabolite structures were elucidated by in silico fragmentation, MS/MS spectral library and comparison of acquired to reference or predicted CCS values using a novel CCS prediction algorithm. Together, each of these steps forms a fully integrated workflow that utilizes the four dimensional data to ensure low level drug metabolites can be annotated.Summary: In this work, a DDA PASEF non targeted LC timsTOF Pro metabolomics workflow was conducted to profile and characterize drug metabolites.References: (1) Djoumbou Feunang et al .; Journal of Cheminform, 2019:11:2
(2) Rouini M. et al .; DARU Journal of Pharmaceutical Sciences, 2013, 21:17.
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